22 - Sklearn Pre-processing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled18.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOlOi/QHFRit1KCRCuq60Jp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MachineLearnia/Python-Machine-Learning/blob/master/22%20-%20Sklearn%20Pre-processing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyias9X7k_Rj",
        "colab_type": "text"
      },
      "source": [
        "# 22/30 Sklearn : Pre-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tPP2Aevle21",
        "colab_type": "text"
      },
      "source": [
        "## 1. Encodage"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WR_kZRCnVjC",
        "colab_type": "text"
      },
      "source": [
        "### Encodage LabelEncoder et LabelBinarizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFsM1ZAWkdIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, LabelBinarizer, OrdinalEncoder, OneHotEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SjxLoV7Bll8h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "67529cf8-830b-4508-ed80-dbe18fb2e662"
      },
      "source": [
        "y = np.array(['chat', 'chien', 'chat', 'oiseau'])\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit_transform(y)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IP2lgap-mYwS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08a0b6f3-c271-4a50-ab19-b1f35d4efc30"
      },
      "source": [
        "encoder.inverse_transform(np.array([0, 0, 2]))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['chat', 'chat', 'oiseau'], dtype='<U6')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5--7T3R6nOem",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "45c0f84b-e6a3-4fed-816e-86ea8efcb13a"
      },
      "source": [
        "encoder = LabelBinarizer()\n",
        "encoder.fit_transform(y)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 0, 0],\n",
              "       [0, 1, 0],\n",
              "       [1, 0, 0],\n",
              "       [0, 0, 1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Yy4VrRpnc9y",
        "colab_type": "text"
      },
      "source": [
        "### Encodage Ordinal et Encodage OneHot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzhb6bmemf6m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "8a0232c8-17ed-4f7a-cf68-27142c666253"
      },
      "source": [
        "X = np.array([['chat', 'poils'],\n",
        "              ['chien', 'poils'],\n",
        "              ['chat', 'poils'],\n",
        "              ['oiseau', 'plumes']])\n",
        "\n",
        "encoder = OrdinalEncoder()\n",
        "encoder.fit_transform(X)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [1., 1.],\n",
              "       [0., 1.],\n",
              "       [2., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DN76sqsjm97-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0f915809-779a-4fbb-878b-5ad1077d8e05"
      },
      "source": [
        "encoder = OneHotEncoder(sparse=False)\n",
        "encoder.fit_transform(X)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 1.],\n",
              "       [0., 1., 0., 0., 1.],\n",
              "       [1., 0., 0., 0., 1.],\n",
              "       [0., 0., 1., 1., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADFisPk-ntrs",
        "colab_type": "text"
      },
      "source": [
        "## 2. Normalisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDJ463bmoD82",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wxie-osLoJ-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()\n",
        "X = iris.data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDkieTz-n3_7",
        "colab_type": "text"
      },
      "source": [
        "### MinMaxScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "317BoWeonpq1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a02bfef3-e793-4211-8d1e-5606e0123559"
      },
      "source": [
        "X_minmax = MinMaxScaler().fit_transform(X)\n",
        "\n",
        "plt.scatter(X[:, 2], X[:, 3])\n",
        "plt.scatter(X_minmax[:, 2], X_minmax[:, 3])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8fee539940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8UfqRHAIn7JK",
        "colab_type": "text"
      },
      "source": [
        "### StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fL67YVJon8j7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "2e131d3c-a925-4924-b741-b9a841518c57"
      },
      "source": [
        "X_stdscl = StandardScaler().fit_transform(X)\n",
        "\n",
        "plt.scatter(X[:, 2], X[:, 3])\n",
        "plt.scatter(X_stdscl[:, 2], X_stdscl[:, 3])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8febc95438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17Nu3Bzjn-OG",
        "colab_type": "text"
      },
      "source": [
        "### RobustScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNsA3137n_Un",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "22c02acc-3ec0-429e-b55d-215583c88c50"
      },
      "source": [
        "X_robust = RobustScaler().fit_transform(X)\n",
        "\n",
        "plt.scatter(X[:, 2], X[:, 3])\n",
        "plt.scatter(X_robust[:, 2], X_robust[:, 3])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8febbedda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QytmUazKpIw7",
        "colab_type": "text"
      },
      "source": [
        "# 3. Polynomial Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wP6CXqe7pDNo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkpGALhhpPAl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "da3c317a-93c1-4a1d-ccfb-f935c64786ca"
      },
      "source": [
        "m = 100\n",
        "X = np.linspace(0, 4, m).reshape((m, 1))\n",
        "y = X**2 + 5*np.cos(X) + np.random.randn(m, 1)\n",
        "\n",
        "model = LinearRegression().fit(X, y)\n",
        "y_pred = model.predict(X)\n",
        "\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, y_pred, c='r', lw=3)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8fe9f9cb70>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGgE9o13p2qc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "4181041e-7e01-4a9e-fd6e-e2b894fd96e0"
      },
      "source": [
        "X_poly = PolynomialFeatures(3).fit_transform(X)\n",
        "model = LinearRegression().fit(X_poly, y)\n",
        "y_pred = model.predict(X_poly)\n",
        "\n",
        "plt.scatter(X, y)\n",
        "plt.plot(X, y_pred, c='r', lw=3)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8fe9f0e828>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Gby2B53qIyM",
        "colab_type": "text"
      },
      "source": [
        "# 4. Discretisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VphiFHiDp_3P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import Binarizer, KBinsDiscretizer"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7ys9gBkqWJg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.linspace(0, 5, 10).reshape((10, 1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dO8OgPA6qaOv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "a2772f99-a635-4a30-ae3f-ffb2a3a2dc95"
      },
      "source": [
        "np.hstack((X, Binarizer(threshold=3).fit_transform(X)))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        ],\n",
              "       [0.55555556, 0.        ],\n",
              "       [1.11111111, 0.        ],\n",
              "       [1.66666667, 0.        ],\n",
              "       [2.22222222, 0.        ],\n",
              "       [2.77777778, 0.        ],\n",
              "       [3.33333333, 1.        ],\n",
              "       [3.88888889, 1.        ],\n",
              "       [4.44444444, 1.        ],\n",
              "       [5.        , 1.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7sIGJizqihx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "ae461672-e4b8-4393-e865-aef7f48bcf4b"
      },
      "source": [
        "KBinsDiscretizer(n_bins=6).fit_transform(X).toarray()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 1., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 1., 0.],\n",
              "       [0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "La28QirlqxVt",
        "colab_type": "text"
      },
      "source": [
        "# 5. Pipelines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bf6Av8duqs-j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXQpcLpeq7Vk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = iris.data\n",
        "y = iris.target\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "noj33VRjrGsb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a8ffa10d-0ca0-4516-8e8b-ebf21f39861a"
      },
      "source": [
        "model = make_pipeline(StandardScaler(), SGDClassifier())\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "model.score(X_test, y_test)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9PTLfrErSGi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV6T5z2yrXgb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "6c49e6ed-ca64-4029-acc2-7b9c8bdda8ac"
      },
      "source": [
        "model = make_pipeline(PolynomialFeatures(),\n",
        "                      StandardScaler(),\n",
        "                      SGDClassifier(random_state=0))\n",
        "params = {\n",
        "    'polynomialfeatures__degree':[2, 3, 4],\n",
        "    'sgdclassifier__penalty':['l1', 'l2']\n",
        "}\n",
        "\n",
        "grid = GridSearchCV(model, param_grid=params, cv=4)\n",
        "\n",
        "grid.fit(X_train, y_train)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('polynomialfeatures',\n",
              "                                        PolynomialFeatures(degree=2,\n",
              "                                                           include_bias=True,\n",
              "                                                           interaction_only=False,\n",
              "                                                           order='C')),\n",
              "                                       ('standardscaler',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('sgdclassifier',\n",
              "                                        SGDClassifier(alpha=0.0001,\n",
              "                                                      average=False,\n",
              "                                                      class_weight=None,\n",
              "                                                      early_stopping=False,\n",
              "                                                      epsi...\n",
              "                                                      n_iter_no_change=5,\n",
              "                                                      n_jobs=None, penalty='l2',\n",
              "                                                      power_t=0.5,\n",
              "                                                      random_state=0,\n",
              "                                                      shuffle=True, tol=0.001,\n",
              "                                                      validation_fraction=0.1,\n",
              "                                                      verbose=0,\n",
              "                                                      warm_start=False))],\n",
              "                                verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'polynomialfeatures__degree': [2, 3, 4],\n",
              "                         'sgdclassifier__penalty': ['l1', 'l2']},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qq7EXPgYrehc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "347c37dd-7703-41dc-a96b-d6687fade71c"
      },
      "source": [
        "grid.score(X_test, y_test)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9736842105263158"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "awOAwg33r11r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}